1. Introduction:

“Forecasting future events is often like searching for a black cat in an
unlit room, that may not even be there”Steven Davidson in The Crystal
Ball
1.1 The requirement for Forecasting:
Increasingly competitive external pressures and complex environments have
resulted in organisational survival becoming more dependantonthe
availability of accurate and timely information. Forecasting is a key
technique used to reduce the uncertainty of the future and providing
businesses with accurate information, regarding key economic and business
variables that they require to make informed and reliableplanning
decisions.


1.2 Types of Forecasting Models:
In order to produce the most reliable forecast a suitable model needs to be
chosen, with an understanding of the method’s applicability to the data and
limitations within the operating environment.Alternative approaches to
forecasting will initially be discussed and then a method for choosing an
appropriate model for the specific data will be outlined.

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Forecasting can be split between a qualitative (judgement) methods and
quantitative (statistical) techniques. As stated by Makridakis, Wheelwright
; Hyndman 1 both approaches are based on the same principle whereby
existing patterns or relationships are identified and these are used as a
foundation for prediction. The divergence lies within the documentation and
processing of information prior to forecasting.

Qualitative procedures rely on subjective assessment and can centre on
personal assessment whereas quantitative models base their predictions on
objective analysis of data and making extrapolations from this.

Makredakis et al2 provide a framework for matching the general
characteristics of the forecasting situation with those of the various
models. For our forecasting situation, constituting a medium-term time
horizon, a single item, +/- 10% accuracy level and 20 years of historic
data, a simple time series model seems appropriate (For further details of
selection decision see section 2).


1.4 General Problems with Forecasting:
“Understanding the limitations of forecasting andsettingrealistic
expectations … are central to making effective use of forecasts”3
As we have noted previously, forecasting is based on establishing patterns
or relationships derived from historic data. Consequently, due to the
dynamic nature of the economic and business environment, a fundamental
disadvantage for modernforecastingisthatthesepatternsand
relationships are prone to dramatic change. Judgement of the magnitude and
timing of these changes becomes a key factor in the accuracy of future
prediction but is not within the scope of most forecasting models.

Academic debate has stemmed from Makridakis’s article “Forecasting: its
role for planning and strategy” regarding the validity of long-term
forecasting in today’s changing environment. It was argued by Makridakis
that the historical data set chosen to extrapolate a forecast was a major
determinate of its accuracy. It was therefore intimated that the key skill
of forecasting was distinguishing long wave cycles (Kondradieff cycle) from
long term trends and establishing an appropriate starting point for
extrapolation. However, as highlighted by Grinyer4 the importance of
technological advancements and human’s ability to influence future events
can not be ignored and could result in the long term equilibrium trend
becoming less relevant as a basis for future forecasting. The consensus of
this debate was that prediction of the general future direction is the best
outcome that can be obtained from extrapolation of past figures in today’s
environment. At least a general direction provides scenario planners with a
starting point for their work.


2. Defining Time Series Models:
Time series methods aim to determine historical patterns and make the
future predictions using a time-based extrapolation of the established
patterns, assuming that these patterns recur over time. It is described as
a black-box system which comprises purely of
input-process-output.


A simple decomposition time series with linear trend line was chosen as our
forecasting model. A moving average cyclical trend line was not used, even
though it produced a closer match to historical data, because of the
inherent limitations of being based solely on recent data points which was
not as applicable to our data set.


The additive decomposition time series has three distinct components:
. Trend.

. Seasonal component.

. Random element.


D = T + S + R
The trend is the long term underlying movement in the variable being
studied whereas the seasonality represents the variability that occurs in
the series during a specified time period, usually one year. The random
element is the difference between the actual data and that predicted by the
combined trend and seasonality.


3. Analysis of time series results:
Initially the time series model was employed to forecast coffee bean
prices. It was observed that seasonality is not a relevant component for
this data set and should not be included in the prediction. An R value of
0.22 emphasizes the large random factor within coffee bean prices.


From the definition above it can be noted that forecasts based purely on
previous patterns do not place any emphasise on the underlying factors
effecting historical data and therefore make no attempt to study these
issues. Makridakis et al5 stated that the difficulty in establishing the
causal variables, the problems in tracking their changes over time and the
low value generated from the understanding are the factors why time-series
does not consider them in its forecast. However, the low R value of our
forecast indicates that further analysis is required to determine the
underlying factors influencing the large random component of our original
decomposition model and this is discussed in Section 4.


Makrdakis’s time frame argument provides us with further insight into the
possibility of a more accurate model. Extrapolating our forecast from an
historic data set of greater scope could allow a long-term trend to be
found and provide a more accurate long-term forecast. However any future
forecasting model must not discount the short term influence of the issues
highlighted in section 4 and their impact on short term price fluctuations.


4. Coffee Industry:
It is the aim of this section to highlight factors within the coffee market
that contribute to the random fluctuations seen when comparing the data to
the time series forecast.

The international coffee market has a long history of price volatility and
the current factors influencing this stem from political and economic
agreements, production improvements, climatic effects and the actions of
competing coffee producing countries.

The International Coffee Agreement (ICA) formed in 1962 introduced quota
systems to provide price stability; however the collapse of this agreement
in 1989 was followed by the establishment of the Association of Coffee
Producing Countries (ACPC) whose policies had the objective of holding back
20% of supply and removing low quality beans from stocks.

Both of these supply side measures highlight the importance for coffee
prices in matching supply with the demand. Without the ACPC’s policies
being successfully implemented coffee bean supply has fluctuated, with
paradoxically both increased and reduced supply resulting in lower prices
in the long run, with intermittent short term volatility.

Improved production techniques has increased supply by approximately 60% in
the previous ten years, however demand has not increased at the same rate
and this has pushed prices down. Conversely climatic conditions, droughts
or floods, reduced the supply of coffee in certain years and led to short
term price increases but resulted in countries such as Vietnam increasing
their production resulting in more price fluctuations.


To what extent these factors are predictable or stable in the future is
uncertain and even with the formation of a new International Coffee
Agreement (2001) the continuation of unpredictable price volatility seems
set to continue. It is interesting to note that since the 2001 ICA there
has been an upward trend in coffee prices and this emphasises Makradakis’s
focus on plausible starting point for trend extrapolation.


5. Conclusion:
It has been shown that the simple time series model is inappropriate for
the data being studied for two reasons. As discussed in Section 4, the
nature of the coffee bean market is not suitably reflectedina
decomposition model due to the complexity of internal andexternal
influences and relationships that create short term price volatility.

Secondly, the historic data set is not great enough to highlight long-term
trends.


Coffee, along with all commodity goods, may be seen to have a long-run
equilibrium trend with short term price volatility around this line. The
size of the oscillations is dependant upon current technological, economic
and political factors that impact on price in the short term.


Empirical evidence on the success of forecasting models has highlighted
that a single forecasting model does not always match the characteristics
of a forecasting situation. Hedging could be utilised in this situation to
incorporate both a time-series model with qualitative proceduresto
determine current influentialfactors,suchasquotaagreements,
productivity, and may result in a reduction in the forecast error.


“He who lives by the crystal ball soon learns to eat ground glass”6
6. Bibliography:
BBCNewswebsite27thNovember2001
www.news.bbc.co.uk/1/hi/business/1678642.stm
Economist “Trouble brewing: Falling coffee prices” March 10th 2001 p5
Grinyer P. H. “Comments on “Forecasting: its role and value for planning
and strategy” by S. Makridakis, International Journal of Forecasting 1996,
Volume 12, pp 539-554
International Coffee Organisation (ICO) “International coffee agreement”
www.ico.org
Makridakis, S. “Forecasting: its role and value for planning and strategy”
International Journal of Forecasting 1996, Volume 12, pp 513-537
Makridarkis. S, Wheelwright S.C, Hyndman R.J. “Forecasting: Methods and
applications” 1998 3rd Edition, New York, John Wiley & Sons
Swift. L, “Quantitative methods for business management and finance” 2001,
1st Edition, Palgrave
Wisniewski. M “Quantitative methods for decision makers” 1997, 2nd Edition,
Pitman
———————–
1 Makridakis S, Wheelwright S & Hyndman R. Forecasting: Methods and
Application p10
2 op cite
3 Makridakis S, Wheelwright S & Hyndman R. Forecasting: Methods and
Application p9
4 Grinyer PH “Comments on “Forecasting: its role and value for planning
strategy” p547
5 Makridakis S, Wheelwright S & Hyndman R. Forecasting: Methods and
Application. Chapter 1
6 Edgar R Fiedler “The 3 R’s of economic forecasting; Irrational,
Irrelevant and Irreverent” 1977